US20080154852A1 - System and method for generating and using a dynamic bloom filter - Google Patents

System and method for generating and using a dynamic bloom filter Download PDF

Info

Publication number
US20080154852A1
US20080154852A1 US11614844 US61484406A US2008154852A1 US 20080154852 A1 US20080154852 A1 US 20080154852A1 US 11614844 US11614844 US 11614844 US 61484406 A US61484406 A US 61484406A US 2008154852 A1 US2008154852 A1 US 2008154852A1
Authority
US
Grant status
Application
Patent type
Prior art keywords
filter
bloom filter
system
false positive
bloom
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
US11614844
Other versions
US7937428B2 (en )
Inventor
Kevin Scott Beyer
Sridhar Rajagopalan
Adriana Zubiri
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
International Business Machines Corp
Original Assignee
International Business Machines Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F12/00Accessing, addressing or allocating within memory systems or architectures
    • G06F12/02Addressing or allocation; Relocation
    • G06F12/08Addressing or allocation; Relocation in hierarchically structured memory systems, e.g. virtual memory systems
    • G06F12/0802Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches
    • G06F12/0864Addressing of a memory level in which the access to the desired data or data block requires associative addressing means, e.g. caches using pseudo-associative means, e.g. set-associative or hashing

Abstract

A dynamic Bloom filter comprises a cascaded set of Bloom filters. The system estimates or guesses a cardinality of input items, selects a number of hash functions based on the desired false positive rate, and allocates memory for an initial Bloom filter based on the estimated cardinality and desired false positive rate. The system inserts items into the initial Bloom filter and counts the bits set as they are inserted. If the number of bits set in the current Bloom filter reaches a predetermined target, the system declares the current Bloom filter full. The system recursively generates additional Bloom filters as needed for items remaining after the initial Bloom filter is filled; items are checked to eliminate duplicates. Each of the set of Bloom filters is individually queried to identify a positive or negative in response to a query. When the system is configured such that the false positive rate of each successive Bloom filter is decreased by one half, the system guarantees a false positive rate of at most twice the desired false positive rate.

Description

    CROSS-REFERENCE TO RELATED APPLICATION
  • The present application relates to co-pending application titled “System And Method For Generating A Cache-Aware Bloom Filter,” Ser. No. 11/614,790 which is filed concurrently herewith, and which is incorporated herein by reference in its entirety.
  • FIELD OF THE INVENTION
  • The present invention generally relates to data structures and in particular to Bloom filters. More specifically, the present invention relates to a Bloom filter that allocates memory as needed while guaranteeing a false positive rate.
  • BACKGROUND OF THE INVENTION
  • A Bloom filter is a space-efficient probabilistic data structure used to test whether an element is a member of a set. This member test can yield false positives but not false negatives. The more elements that are added to the set contained in the Bloom filter, the larger the probability of false positives. Bloom filters have a strong space advantage over other data structures, such as self-balancing search trees, tries, hash tables, or simple arrays or linked lists of the entries.
  • A Bloom filter is an approximate encoding of a set of items or keys using a bit vector of b bits. During encoding, the item is hashed to a number between 1 to b and the corresponding bit in the bit vector is set. To check if an item is a member of the set, the item is hashed and the status of the bit is checked. If the bit is not set, then the item is definitely not in the set. If the bit is set, then either the item is in the set or the hash value of this item collided with the hash value of some other item that is in the set. Because of hash collisions, a Bloom filter can produce false positives (the item is reported as in the set, but it is not), but it never produces false negatives (the item is in the set, but not reported).
  • Conventional approaches improve the effectiveness of a Bloom filter by hashing each item several times with independent hash functions. For example, k hashes are used. To encode an item x, the k bits in the bit vector that correspond to hi(x) for 1≦i≦k are set. (The same bit may be picked any number of times). To check if item y is a member of the set, item y is hashed k times using the same hash functions. The bit corresponding to hi(x) is examined to determine whether it is set for all 1≦i≦k. If any of the k bits are not set, then y cannot be a member of the set; otherwise, all k bits are set and item y is either in the set or a false positive.
  • Conventional Bloom filters have control points comprising the number of items in the input (n), the amount of memory (b), the number of hash functions (k), and the probability of a false positive (i.e., the false positive rate or fpr). Fixing the size of the input allows the choice of two of the other control point parameters. Memory and the number of hash functions are related. If the number of hashes is fixed and memory is increased, the false positive rate continually decreases. However, if the memory is fixed and the number of hash functions is increased, the false positive rate exhibits a minimum when an expected density (i.e., the percentage of bits set to 1) for the conventional Bloom filter is approximately 50%.
  • Although conventional Bloom filter technology has proven to be useful, it would be desirable to present additional improvements. A conventional Bloom filter is built and then populated with a set of items or keys. To build a conventional Bloom filter, a user has to know approximately how many keys will populate the conventional Bloom filter to know how much memory to allocate to the conventional Bloom filter. However, in many applications the number of keys is not known prior to building the conventional Bloom filter. Consequently, a user is forced to overestimate the number of keys anticipated for the conventional Bloom filter, leading to inefficient use of memory. Furthermore, inefficient use of memory may lead to a false positive rate that is less than optimum.
  • Conventional Bloom filters require an accurate estimate of the cardinality of the initial input set. The cardinality is the number of distinct values for a multi-set. The size of the initial input, along with the false-positive rate, determines the amount of memory allocated to encode the set. If the cardinality estimate is wrong, the false positive rate can be much higher than expected.
  • For example, a conventional Bloom filter may have a target false positive rate of 1/256 for an optimal 8 hashes and a target filter density of 50%. If the actual cardinality is as little as 2 times that of the cardinality estimate, the false positive rate can be 25 times what was expected. If the actual cardinality is 4 times the cardinality estimate, the false positive rate jumps to 150 times the expected value. In this case, over half of the negative results are returned as false positives and the Bloom filter is not particularly useful.
  • What is therefore needed is a system, a computer program product, and an associated method for generating and using a dynamic Bloom filter that self-sizes as more keys are entered in the Bloom filter. The need for such a solution has heretofore remained unsatisfied.
  • SUMMARY OF THE INVENTION
  • The present invention satisfies this need, and presents a system, a service, a computer program product, and an associated method (collectively referred to herein as “the system” or “the present system”) for generating and using a dynamic Bloom filter.
  • The present system estimates a cardinality of a set of input items based on an allowable false positive rate, allocates memory for an initial Bloom filter and selects a number of hash functions based on the estimated cardinality. The present system inserts items from the set of input items into the initial Bloom filter and counts the items set in the initial Bloom filter as the items are inserted. If a number of items set in the initial Bloom filter has reached a predetermined target, declaring the initial Bloom filter full.
  • If there exists a remaining portion comprising additional items in the set of input items that remain for insertion into the dynamic Bloom filter, the present system allocates memory for a next Bloom filter based on an estimated cardinality of the remaining portion or a predetermined memory growth technique. The present system inserts non-duplicate items in the next Bloom filter and counts the bits changed from 0 to 1 as the items are inserted in the next Bloom filter, wherein the non-duplicate items are not duplicates of the items previously inserted in the initial Bloom filter.
  • If a number of items set in the next Bloom filter has reached a predetermined target, the present system declares the next Bloom filter full. The present system recursively generates additional Bloom filters until the remaining portion is inserted into any of the next Bloom filter and the additional Bloom filters such that the dynamic Bloom filter comprises the initial Bloom filter and any of the next Bloom filter and the additional Bloom filters and no duplicate items are inserted in the dynamic Bloom filter.
  • In one embodiment, the system does not search for duplicates in the previous filters. Detecting duplicate input items can reduce the amount of memory consumed because each unique item is encoded in exactly one filter. When duplicates are not detected, an item may be encoded in multiple filters. If the input contains no duplicates the or few duplicates the time spent searching for duplicates can be saved at the expense of an additional memory.
  • The predetermined target is approximately 50% of the bits in any of the initial Bloom filter, the next Bloom filter, and the additional Bloom filters.
  • In one embodiment, the predetermined memory growth technique comprises a linear growth technique such that each of the next Bloom filter and the additional Bloom filters are approximately a same size as the initial Bloom filter. In another embodiment, the predetermined memory growth technique comprises an exponential growth technique, such that each of the next Bloom filter and the additional Bloom filters are a predetermined factor larger than a preceding Bloom filter. The predetermined factor is, for example 2.
  • Counting the items inserted in the initial Bloom filter, the next Bloom filter, and the additional Bloom filters comprises counting bits as the bits are changed from 0 to 1.
  • Using the dynamic Bloom filter comprises receiving a query for any of the items in the dynamic Bloom filter and selecting any of the initial Bloom filter, the next Bloom filter, or the additional Bloom filters to query. If the query of the selected Bloom filter returns positive, return positive for the query of the dynamic Bloom filter. Otherwise, query each of any remaining of the initial Bloom filter, the next Bloom filter, or the additional Bloom filters to query until a positive is returned, and, if no positive is returned, return a negative for the query of the dynamic Bloom filter.
  • The last of the initial Bloom filter, the next Bloom filter, and the additional Bloom filters comprises the largest percentage of items in the dynamic Bloom filter and is selected first for querying because the last of the initial Bloom filter, the next Bloom filter, and the additional Bloom filters has the highest probability of containing a queried item. An order for querying the initial Bloom filter, the next Bloom filter, and the additional Bloom filters proceeds last to first, successively querying the initial Bloom filter, the next Bloom filter, and the additional Bloom filters by decreasing size.
  • In another embodiment, the false positive rate is adjusted. If the false positive rate of each successive Bloom filter is divided in half, then a fixed false positive rate of the set of Bloom filters is guaranteed, regardless of the input set size. As an illustration, if f1=ftarget/2 and fi+1=fi/2, then factual≦sum fi<ftarget is guaranteed. If the false positive rate is kept constant for each filter, then the false positive rate will increase linearly with the number of Bloom filters created.
  • DESCRIPTION OF THE DRAWINGS
  • The various features of the present invention and the manner of attaining them will be described in greater detail with reference to the following description, claims, and drawings, wherein reference numerals are reused, where appropriate, to indicate a correspondence between the referenced items, and wherein:
  • FIG. 1 is a schematic illustration of an exemplary operating environment in which a dynamic Bloom filter system of the present invention can be used;
  • FIG. 2 is a block diagram of the high-level architecture of the dynamic Bloom filter system of FIG. 1;
  • FIG. 3 represents a process flow chart illustrating a method of operation of the dynamic Bloom filter system of FIGS. 1 and 2 in generating a dynamic Bloom filter; and
  • FIG. 4 is a schematic illustration portraying the operation of the dynamic Bloom filter system of FIGS. 1 and 2 in using the dynamic Bloom filter.
  • DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
  • FIG. 1 portrays an exemplary overall environment in which a system, a computer program product, and an associated method (the dynamic Bloom filter system 10 or the “system 10”) for generating and using a dynamic Bloom filter according to the present invention may be used. System 10 comprises a software programming code or a computer program product that is typically embedded within, or installed on a server 15. Alternatively, system 10 can be saved on a suitable storage medium such as a diskette, a CD, a hard drive, or like devices.
  • System 10 can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements. In one embodiment, system 10 is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc.
  • Furthermore, system 10 can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk—read only memory (CD-ROM), compact disk—read/write (CD-R/W) and DVD.
  • A data processing system suitable for storing and/or executing program code includes at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories that provide temporary storage of at least some program code to reduce the number of times code is retrieved from bulk storage during execution.
  • Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers.
  • Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modems, and Ethernet cards are just a few of the currently available types of network adapters.
  • In an exemplary use of system 10, a database management system 100 comprises a database (dB) 20 and system 10. Users, such as remote Internet users, are represented by a variety of computers such as computers 25, 30, 35, and can access the host server 15 through a network 40. Computers 25, 30, 35, access system 10 to determine whether an element is a member of a set stored in system 10. System 10 returns a negative if the element is not a member of the set. System 10 returns a positive if the element is in the set. System 10 may return a false positive if the element is not in the set. System 10 does not return false negatives. While described in terms of the database management system 100, it should be clear that system 10 is applicable as well to, for example, any implementation in which a Bloom filter may be used.
  • System 10 provides a guaranteed false positive rate without requiring a fixed estimate for the cardinality of an initial input set. Conventional Bloom filters allocate one filter based upon the input cardinality. System 10 allocates memory as needed. If the initial cardinality estimate is accurate, then system 10 behaves similar to a conventional Bloom filter in terms of memory, requiring only a small additional CPU expense to track usage. When additional memory allocations are used, memory and CPU usage increase compared to a conventional Bloom filter. However, system 10 can guarantee a false positive rate.
  • For example, assume a set S is encoded in two filters using system 10 with duplicate detection such that the filters encode two disjoint sets S1, and S2. System 10 can encode S1 and S2 using Bloom filters based on the cardinality of each set. The Bloom filter for S1 can use the same hash functions as S2 because S1 and S2 are disjoint; no independence is required. To test whether xεS1∪S2, system 10 tests both filters: xεF(S1) or xεF(S2). The false positive rate (denoted fpr) for the combined test of S1 and S2 is as follows (where fp denotes false positive and f is the fpr of each filter):
  • P [ fp from union of S 1 and S 2 ] = P [ fp from S 1 or S 2 ] P [ fp from S 1 ] + P [ fp from S 2 ] - = P [ fp from S 1 and S 2 ] ( inclusion / exclusion ) = P [ fp from S 1 ] + P [ fp from S 2 ] - P [ fp from S 1 ] · P [ fp from S 2 ] = 2 A ¨ - A ¨ 2 Z . 2 A ¨ .
  • Consequently, the false positive rate (fpr) of the union of n sets is at most n times the false positive rate of one Bloom filter.
  • FIG. 2 illustrates a high-level hierarchy of system 10. System 10 comprises a build module 205 and a use module 210. The build module 205 generates the dynamic Bloom filter 215 and populates the dynamic Bloom filter 215 using input items 220. The dynamic Bloom filter 215 comprises one or more Bloom filters in a cascade. The build module 205 comprises a cardinality estimator 230, a capacity check module 225, a duplicate detection module 227, an insertion module 235, and a filter allocation module 245. The use module 210 provides query access by a user to the dynamic Bloom filter 215. The use module 210 comprises a Bloom filter query module 250 and a Bloom filter selection module 255.
  • FIG. 3 illustrates a method 300 of the build module 205 of system 10 in generating the dynamic Bloom filter 215. The cardinality estimator 230 increments a Bloom filter counter, i, and estimates or guesses a cardinality, ni, for the input items 220 (step 304). In one embodiment, the allocation module 245 determines a size for the ith Bloom filter using linear growth such that the ith Bloom filter is approximately the same size as the initial Bloom filter. In another embodiment, the allocation module 245 determines a size for the ith Bloom filter using exponential growth such that the ith Bloom filter is some predetermined factor larger than the (i−1)th Bloom filter. Any suitable technique may be used to determine the size of the ith Bloom filter.
  • The filter allocation module 245 determines a number of hash functions, ki, based on the false positive rate of filter i, fi, and allocates memory for a Bloom filter in the dynamic Bloom filter 215 based on ni and fi (step 306):
  • k = lg ( 1 A ¨ ) .
  • As used herein, “lg” is interchangeable with “log2”.
  • The capacity check module 225 determines whether additional items remain to be encoded (step 312). If no items remain, the capacity check module 225 causes the build module 205 to exit (step 314). If an item remains, the capacity check module 225 determines whether the target number of bits are set in the current filter 215 (step 316). For a memory-optimal Bloom filter, the predetermined target number of bits for the initial Bloom filter is approximately 50% of the maximum number of bits (and therefore approximately ni items have been inserted in the Bloom filter). If the filter is at capacity, the capacity check module 225 returns to step 304. If the filter has room to encode the item (step 316), method 300 proceeds to step 332.
  • At step 332, the duplicate detection module 227 considers an item from the input set and checks if the item was previously encoded in any prior filter using method 400. If it has already been encoded, then it is returns to step 312 (step 332 and 334). Otherwise, method 300 proceeds to step 310 (step 308).
  • At step 310, the insertion module 235 inserts the current item in the current Bloom filter 215, and returns to step 312. The same hash codes can be used for all the Bloom filters in the dynamic Bloom filter 215. Consequently, there is no need to hash additional times or use independent hash functions even though two or more Bloom filters are used.
  • The overall false positive rate for the dynamic Bloom filter 215 comprising n Bloom filters is at most ngÄ. Inclusion/exclusion reduces the false positive rate below ngÄ because a false positive in more than one filter is counted only once. Furthermore, the nth Bloom filter is typically only partially full and thus has a false positive rate lower than f.
  • Given sufficient memory and hash functions, the false positive rate of the dynamic Bloom filter 215 can be guaranteed, regardless of the number of filters used. The false positive rate is at most twice the false positive rate for the initial Bloom filter.
  • If the false positive rate for the initial Bloom filter is f, f/2 for the second filter, f/n for the nth filter, etc:
  • Total Z . fpr i = 1 # Z . filters A ¨ 2 i < 2 A ¨
  • This false positive rate for the dynamic Bloom filter 215 is achieved by sizing each successive Bloom filter in the dynamic Bloom filter 215 such that the false positive rate of each Bloom filter, fi, is half the false positive rate of the previous Bloom filter, f(i−1). When using memory-minimal Bloom filters, this constraint translates into one additional hash function and one extra bit set per entry, and a corresponding increase in memory to encode the estimated number of items at this false positive rate.
  • To guarantee the false positive rate of the dynamic Bloom filter, the false positive rate of each filter is half that of the previous filter. When the false positive rate is decreased, the required number of bits per entry is increased. The number of bits per entry of the filter i is −ln(2)*lg(fi), where fi=f1/2i−1. Therefore, filter i uses (1+(i−1)/−lg(f)) times more bits per entry than the first filter. Consequently, the relative increase in memory depends upon the original false positive rate, f: the lower the false positive rate, the less significant the increase in memory. For f=1/256, the second filter would use 12.5% more bits per entry than the first filter.
  • System 10 comprises a technique to encode a set or multi-set of unknown size in a series of Bloom filters in one pass of the input items 220. With only a small overhead, the technique produces the same results as a conventional Bloom filter when the initial cardinality estimate is accurate or over estimated. When the initial cardinality estimate is low, the dynamic Bloom filter 215 responds to the underestimate more gracefully than a conventional Bloom filter. System 10 can choose to pay a linear increase in the false positive rate with each estimation error. Otherwise, system 10 can guarantee the false positive rate using additional memory and hashing.
  • FIG. 4 illustrates a method of the use module 210 in using the dynamic Bloom filter 215. The Bloom filter query module 250 receives a query for an item (step 405). The Bloom filter selection module 255 selects any Bloom filter, filter i, in the dynamic Bloom filter 215 to query (step 410) that has not yet been selected. In one embodiment, the Bloom filter selection module 255 selects the Bloom filter that is most likely to contain the query item. In another embodiment, the Bloom filter selection module 225 selects the filter with the most entries encoded in it by the build module 205.
  • The probe module 260 determines if the query item is found in filter i by hashing ki times and checking if all ki bits are set in filter i (step 412). If the item is found at step 415, the use module 210 returns a positive result (i.e., that the item might be in the encoded set) (step 420). Otherwise, the probe module 260 inquires whether additional filters remain to be tested (step 425). If so, method 400 iteratively returns to step 410. Otherwise, method 400 returns a negative result (i.e., the query item is definitely not in the encoded set) (step 430).
  • It is to be understood that the specific embodiments of the invention that have been described are merely illustrative of certain applications of the principle of the present invention. Numerous modifications may be made to the system and method for generating and using a dynamic Bloom filter described herein without departing from the spirit and scope of the present invention.

Claims (2)

  1. 1. A method of implementing a dynamic Bloom filter comprised of one or more filters, the method comprising:
    generating the dynamic Bloom filter according to the following steps:
    initializing a filter counter to zero;
    setting a filter capacity indicator to a value of true;
    while an input item is available, iteratively performing if the filter capacity indicator is true then,
    incrementing the filter counter by one;
    allocating a memory for a current filter based upon a cardinality estimate and a false positive rate;
    determining a number bits to be set for the input item based upon the false positive rate for the current filter;
    inserting the input item into the current filter; and
    setting the filter capacity indicator to false; otherwise
    checking the selected filter to determine if the input item is contained therein;
    ignoring the input item if the input item is found in the selected filter;
    inserting the input item into the current filter if the input item was not found in any selected filter; and
    setting the filter capacity indicator to true if the item is inserted in the current filter and current filter is at capacity thereafter; and
    using the dynamic Bloom filter according to the following steps:
    receiving a query for an item;
    applying the query to a selected filter in the dynamic Bloom filter;
    upon determination that the query of the selected filter returns apositive result, returning the positive result for the query; and
    returning a negative result for the query of the dynamic Bloom filter if no selected filter returns a positive result.
  2. 2-20. (canceled)
US11614844 2006-12-21 2006-12-21 System and method for generating and using a dynamic bloom filter Expired - Fee Related US7937428B2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US11614844 US7937428B2 (en) 2006-12-21 2006-12-21 System and method for generating and using a dynamic bloom filter

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11614844 US7937428B2 (en) 2006-12-21 2006-12-21 System and method for generating and using a dynamic bloom filter
US12134148 US8209368B2 (en) 2006-12-21 2008-06-05 Generating and using a dynamic bloom filter

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US12134148 Continuation US8209368B2 (en) 2006-12-21 2008-06-05 Generating and using a dynamic bloom filter

Publications (2)

Publication Number Publication Date
US20080154852A1 true true US20080154852A1 (en) 2008-06-26
US7937428B2 US7937428B2 (en) 2011-05-03

Family

ID=39544342

Family Applications (2)

Application Number Title Priority Date Filing Date
US11614844 Expired - Fee Related US7937428B2 (en) 2006-12-21 2006-12-21 System and method for generating and using a dynamic bloom filter
US12134148 Expired - Fee Related US8209368B2 (en) 2006-12-21 2008-06-05 Generating and using a dynamic bloom filter

Family Applications After (1)

Application Number Title Priority Date Filing Date
US12134148 Expired - Fee Related US8209368B2 (en) 2006-12-21 2008-06-05 Generating and using a dynamic bloom filter

Country Status (1)

Country Link
US (2) US7937428B2 (en)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080065822A1 (en) * 2006-09-08 2008-03-13 Murukanandam K P Determining the Presence of a Pre-Specified String in a Message
US20080065639A1 (en) * 2006-08-25 2008-03-13 Netfortis, Inc. String matching engine
US20080306903A1 (en) * 2007-06-08 2008-12-11 Microsoft Corporation Cardinality estimation in database systems using sample views
US20090187546A1 (en) * 2008-01-21 2009-07-23 International Business Machines Corporation Method, System and Computer Program Product for Duplicate Detection
US20100098318A1 (en) * 2008-10-20 2010-04-22 Jpmorgan Chase Bank, N.A. Method and System for Duplicate Check Detection
EP2362317A1 (en) * 2008-12-19 2011-08-31 NEC Corporation Information processing device
WO2011136767A1 (en) * 2010-04-28 2011-11-03 Hewlett-Packard Development Company, L.P. Protecting the information encoded in a bloom filter using encoded bits of data
US20120310960A1 (en) * 2011-06-06 2012-12-06 Fujitsu Limited Non-transitory, computer readable storage medium, search method, and search apparatus
US20130219117A1 (en) * 2012-02-16 2013-08-22 Peter Macko Data migration for composite non-volatile storage device
US20140304238A1 (en) * 2013-04-05 2014-10-09 Nokia Corporation Method and apparatus for detecting duplicate messages
US20140344934A1 (en) * 2013-05-17 2014-11-20 Hewlett-Packard Development Company, L.P. Bloom filter with memory element
US9092447B1 (en) * 2008-10-20 2015-07-28 Jpmorgan Chase Bank, N.A. Method and system for duplicate detection
US20160110292A1 (en) * 2014-10-21 2016-04-21 Samsung Electronics Co., Ltd. Efficient key collision handling

Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7937428B2 (en) * 2006-12-21 2011-05-03 International Business Machines Corporation System and method for generating and using a dynamic bloom filter
US8484438B2 (en) * 2009-06-29 2013-07-09 Oracle America, Inc. Hierarchical bloom filters for facilitating concurrency control
US8134934B2 (en) * 2009-09-21 2012-03-13 Alcatel Lucent Tracking network-data flows
US8332367B2 (en) 2010-10-20 2012-12-11 International Business Machines Corporation Parallel data redundancy removal
EP2656244A4 (en) * 2010-12-20 2015-10-21 Ericsson Telefon Ab L M Searching in peer to peer networks
US8498995B1 (en) * 2011-03-24 2013-07-30 Emc Corporation Optimizing data retrieval during event data query processing
US20130173853A1 (en) * 2011-09-26 2013-07-04 Nec Laboratories America, Inc. Memory-efficient caching methods and systems
US8370460B1 (en) 2012-01-10 2013-02-05 Edgecast Networks, Inc. Optimizing multi-hit caching for long tail content
US9519614B2 (en) 2012-01-10 2016-12-13 Verizon Digital Media Services Inc. Multi-layer multi-hit caching for long tail content
US9465826B2 (en) * 2012-11-27 2016-10-11 Hewlett Packard Enterprise Development Lp Estimating unique entry counts using a counting bloom filter
CN103927124B (en) * 2013-01-15 2018-03-13 深圳市腾讯计算机系统有限公司 Apparatus and method for disk access and to control organized Hash
US20150178769A1 (en) * 2013-12-24 2015-06-25 Google Inc. Systems and methods for audience measurement
US9740714B2 (en) 2014-02-06 2017-08-22 International Business Machines Corporation Multilevel filters for cache-efficient access
US9940356B2 (en) * 2014-07-31 2018-04-10 International Business Machines Corporation Efficient join-filters for parallel processing
US9608810B1 (en) 2015-02-05 2017-03-28 Ionic Security Inc. Systems and methods for encryption and provision of information security using platform services
US10049156B2 (en) 2015-05-05 2018-08-14 International Business Machines Corporation Cross-domain adaptation using bloom filters

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US26268A (en) * 1859-11-29 Machine for riving- basket-splints
US33803A (en) * 1861-11-26 Improvement in maneuvering heavy guns
US108368A (en) * 1870-10-18 Improvement in gates
US219929A (en) * 1879-09-23 Improvement in spring-hoes for seeding-machines
US20030026268A1 (en) * 2000-11-28 2003-02-06 Siemens Technology-To-Business Center, Llc Characteristic routing
US20030208665A1 (en) * 2002-05-01 2003-11-06 Jih-Kwon Peir Reducing data speculation penalty with early cache hit/miss prediction
US20050033803A1 (en) * 2003-07-02 2005-02-10 Vleet Taylor N. Van Server architecture and methods for persistently storing and serving event data
US20050086520A1 (en) * 2003-08-14 2005-04-21 Sarang Dharmapurikar Method and apparatus for detecting predefined signatures in packet payload using bloom filters
US20050108368A1 (en) * 2003-10-30 2005-05-19 Aditya Mohan Method and apparatus for representing data available in a peer-to-peer network using bloom-filters
US20050219929A1 (en) * 2004-03-30 2005-10-06 Navas Julio C Method and apparatus achieving memory and transmission overhead reductions in a content routing network
US20080155229A1 (en) * 2006-12-21 2008-06-26 Kevin Scott Beyer System and method for generating a cache-aware bloom filter
US20080243800A1 (en) * 2006-12-21 2008-10-02 International Business Machines Corporation System and method for generating and using a dynamic blood filter

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US26268A (en) * 1859-11-29 Machine for riving- basket-splints
US33803A (en) * 1861-11-26 Improvement in maneuvering heavy guns
US108368A (en) * 1870-10-18 Improvement in gates
US219929A (en) * 1879-09-23 Improvement in spring-hoes for seeding-machines
US20030026268A1 (en) * 2000-11-28 2003-02-06 Siemens Technology-To-Business Center, Llc Characteristic routing
US20030208665A1 (en) * 2002-05-01 2003-11-06 Jih-Kwon Peir Reducing data speculation penalty with early cache hit/miss prediction
US20050033803A1 (en) * 2003-07-02 2005-02-10 Vleet Taylor N. Van Server architecture and methods for persistently storing and serving event data
US20050086520A1 (en) * 2003-08-14 2005-04-21 Sarang Dharmapurikar Method and apparatus for detecting predefined signatures in packet payload using bloom filters
US20050108368A1 (en) * 2003-10-30 2005-05-19 Aditya Mohan Method and apparatus for representing data available in a peer-to-peer network using bloom-filters
US20050219929A1 (en) * 2004-03-30 2005-10-06 Navas Julio C Method and apparatus achieving memory and transmission overhead reductions in a content routing network
US20080155229A1 (en) * 2006-12-21 2008-06-26 Kevin Scott Beyer System and method for generating a cache-aware bloom filter
US20080243800A1 (en) * 2006-12-21 2008-10-02 International Business Machines Corporation System and method for generating and using a dynamic blood filter
US20080243941A1 (en) * 2006-12-21 2008-10-02 International Business Machines Corporation System and method for generating a cache-aware bloom filter

Cited By (25)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080065639A1 (en) * 2006-08-25 2008-03-13 Netfortis, Inc. String matching engine
US20080065822A1 (en) * 2006-09-08 2008-03-13 Murukanandam K P Determining the Presence of a Pre-Specified String in a Message
US8069304B2 (en) * 2006-09-08 2011-11-29 Intel Corporation Determining the presence of a pre-specified string in a message
US20080306903A1 (en) * 2007-06-08 2008-12-11 Microsoft Corporation Cardinality estimation in database systems using sample views
US20090187546A1 (en) * 2008-01-21 2009-07-23 International Business Machines Corporation Method, System and Computer Program Product for Duplicate Detection
US8055633B2 (en) * 2008-01-21 2011-11-08 International Business Machines Corporation Method, system and computer program product for duplicate detection
US20100098318A1 (en) * 2008-10-20 2010-04-22 Jpmorgan Chase Bank, N.A. Method and System for Duplicate Check Detection
US8639017B1 (en) * 2008-10-20 2014-01-28 Jpmorgan Chase Bank, N.A. Method and system for duplicate check detection
US9092447B1 (en) * 2008-10-20 2015-07-28 Jpmorgan Chase Bank, N.A. Method and system for duplicate detection
US8391584B2 (en) * 2008-10-20 2013-03-05 Jpmorgan Chase Bank, N.A. Method and system for duplicate check detection
EP2362317A1 (en) * 2008-12-19 2011-08-31 NEC Corporation Information processing device
EP2362317A4 (en) * 2008-12-19 2015-01-07 Nec Corp Information processing device
WO2011136767A1 (en) * 2010-04-28 2011-11-03 Hewlett-Packard Development Company, L.P. Protecting the information encoded in a bloom filter using encoded bits of data
US8904176B2 (en) 2010-04-28 2014-12-02 Hewlett-Packard Development Company Protecting the information encoded in a bloom filter using encoded bits of data
US8423562B2 (en) * 2011-06-06 2013-04-16 Fujitsu Limited Non-transitory, computer readable storage medium, search method, and search apparatus
US20120310960A1 (en) * 2011-06-06 2012-12-06 Fujitsu Limited Non-transitory, computer readable storage medium, search method, and search apparatus
US9710397B2 (en) * 2012-02-16 2017-07-18 Apple Inc. Data migration for composite non-volatile storage device
CN104115133A (en) * 2012-02-16 2014-10-22 苹果公司 Data migration for composite non-volatile storage device
KR101620773B1 (en) * 2012-02-16 2016-05-12 애플 인크. Data migration for composite non-volatile storage device
US20130219117A1 (en) * 2012-02-16 2013-08-22 Peter Macko Data migration for composite non-volatile storage device
US20140304238A1 (en) * 2013-04-05 2014-10-09 Nokia Corporation Method and apparatus for detecting duplicate messages
US9171153B2 (en) * 2013-05-17 2015-10-27 Hewlett-Packard Development Company, L.P. Bloom filter with memory element
US20140344934A1 (en) * 2013-05-17 2014-11-20 Hewlett-Packard Development Company, L.P. Bloom filter with memory element
US20160110292A1 (en) * 2014-10-21 2016-04-21 Samsung Electronics Co., Ltd. Efficient key collision handling
US9846642B2 (en) * 2014-10-21 2017-12-19 Samsung Electronics Co., Ltd. Efficient key collision handling

Also Published As

Publication number Publication date Type
US7937428B2 (en) 2011-05-03 grant
US20080243800A1 (en) 2008-10-02 application
US8209368B2 (en) 2012-06-26 grant

Similar Documents

Publication Publication Date Title
Goldschmidt et al. Polynomial algorithm for the k-cut problem
US7103731B2 (en) Method, system, and program for moving data among storage units
US7797323B1 (en) Producing representative hashes for segments of a file
US6768991B2 (en) Searching for sequences of character data
Cong et al. Farmer: Finding interesting rule groups in microarray datasets
US6996575B2 (en) Computer-implemented system and method for text-based document processing
Charikar et al. Better streaming algorithms for clustering problems
Gecsei et al. Evaluation techniques for storage hierarchies
US6266658B1 (en) Index tuner for given workload
Wackerbauer et al. A comparative classification of complexity measures
US7320000B2 (en) Method and apparatus for populating a predefined concept hierarchy or other hierarchical set of classified data items by minimizing system entrophy
Huang et al. Fragment allocation in distributed database design
US20030115183A1 (en) Estimation and use of access plan statistics
US5828999A (en) Method and system for deriving a large-span semantic language model for large-vocabulary recognition systems
US20050235356A1 (en) System and method for analyzing a pattern in a time-stamped event sequence
US20040249781A1 (en) Techniques for graph data structure management
US6643754B1 (en) System and method for dynamically allocating computer memory
US6446183B1 (en) Systems and methods for persistent and robust memory management
US6360361B1 (en) Field reordering to optimize cache utilization
US20060149915A1 (en) Memory management technique
US7644052B1 (en) System and method of building and using hierarchical knowledge structures
US20070028033A1 (en) Method for identifying data characteristics for flash memory
US20110264712A1 (en) Copy planning in a concurrent garbage collector
US20110047144A1 (en) System, method, and apparatus for parallelizing query optimization
US7921086B1 (en) Deterministic space management in deduplication applications

Legal Events

Date Code Title Description
AS Assignment

Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BEYER, KEVIN S.;RAJAGOPALAN, SRIDHAR;ZUBIRI, ADRIANA;REEL/FRAME:018895/0511;SIGNING DATES FROM 20061218 TO 20070123

Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:BEYER, KEVIN S.;RAJAGOPALAN, SRIDHAR;ZUBIRI, ADRIANA;SIGNING DATES FROM 20061218 TO 20070123;REEL/FRAME:018895/0511

REMI Maintenance fee reminder mailed
LAPS Lapse for failure to pay maintenance fees
FP Expired due to failure to pay maintenance fee

Effective date: 20150503